memory-contract

Name: memory-contract
Availability: InStock
Author: wolvesfield

Standardize AI memory management with a 5-sector model and progressive retrieval.

Updated Aug 23, 2026

Search Tags:#knowledge management #ai #agent #protocol #retrieval #memory

Ready to run

npx skills add https://github.com/wolvesfield/CIPHER-MCP --skill memory-contract-wolvesfield

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory-contract
Source: https://github.com/wolvesfield/CIPHER-MCP/tree/main/core/skills/memory-contract
Command: npx skills add https://github.com/wolvesfield/CIPHER-MCP --skill memory-contract-wolvesfield

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a standardized protocol for how AI agents manage their memory, ensuring consistent and efficient storage and retrieval of information across different agents and tasks.

Core Features & Use Cases

5-Sector Memory Model: Organizes memories into episodic, semantic, procedural, emotional, and reflective sectors for better management.
Progressive Retrieval: Implements a 3-layer retrieval system to efficiently access relevant context without overwhelming the AI.
Composite Scoring: Ranks memories based on salience, recency, and coactivation for optimal relevance.
Use Case: When an agent needs to recall past decisions or project knowledge, this protocol ensures it retrieves the most pertinent information efficiently, preventing context overload and improving decision-making.

Quick Start

Use the memory-contract skill to retrieve active fleet plans and constraints at the start of a new session.

Frequently Asked Questions about memory-contract

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I standardize AI agent memory management across different sessions?▼

You can standardize AI agent memory management by implementing a unified protocol that governs storage and retrieval procedures across sessions. This protocol uses a 5-sector model and progressive retrieval to ensure agents consistently handle episodic, semantic, procedural, emotional, and reflective memories.

What is progressive retrieval in AI memory systems?▼

Progressive retrieval in AI memory systems is a 3-layer mechanism that efficiently accesses relevant context without overwhelming the agent. It ranks memories using composite scoring based on salience, recency, and coactivation to deliver optimal relevance during context retrieval.

How do I prevent context overload when an AI agent recalls past project decisions?▼

To prevent context overload when an AI agent recalls past decisions, use a protocol that employs progressive retrieval and composite scoring. This approach ranks memories by salience, recency, and coactivation, ensuring only the most pertinent information is retrieved.

Can I use a knowledge graph for temporal queries in AI agent memory?▼

Yes, you can use temporal knowledge graph queries within an AI agent memory protocol. The system governs memory consolidation and retrieval across multiple sectors, enabling structured temporal queries for episodic and semantic knowledge.

Does the AI memory protocol handle session boot and context retrieval automatically?▼

Yes, the AI memory protocol explicitly addresses session boot and context retrieval procedures. It retrieves active fleet plans and constraints at the start of a new session, applying defined salience and decay rates to manage memory relevance.

What are the limitations of using a unified memory protocol for AI agents?▼

A unified memory protocol for AI agents requires strict adherence to defined salience and decay rates across five memory sectors. Memory consolidation processes must be properly governed to prevent retrieval degradation, and agents need full compatibility with the 3-layer retrieval system.